Digital technology possesses varying degrees of intelligence and autonomy. Over time, these levels of intellect and autonomy can be expected to increase. Therefore, it is conceivable that some version of the reality depicted in science fiction, where humanity is immersed in an all-seeing, all-knowing, and sometimes hostile, digital environment, may be realized.
Most human-machine interaction models assume that humans will readily meld into the mix of software agents, robots and electronic systems, despite a history in which human limits have often bounded the potential of advanced technology. In these models, it is the responsibility of the human user to acquire a mental model of a machine in order to engage that machine in a productive interaction. Consequently, given advances in raw computing capabilities, human limitations have become the dominant factor in the following ratio:
      Overall    ⁢                  ⁢    System    ⁢                  ⁢    Value    =                    Productive        ⁢                                  ⁢        Output        ⁢                                  ⁢        Human            -              Machine        ⁢                                  ⁢        System                    Overall      ⁢                          ⁢      Resources      ⁢                          ⁢      Expended      ⁢                          ⁢      by      ⁢                          ⁢      Human      
By reversing the situation so that the machine acquires a mental model of the human, technical factors such as computational power, storage capacity and communications bandwidth can become the primary factors driving the above ratio. Therefore, without the drag imposed by human limitations, the systems value can be freed to grow in proportion to the technical capabilities (e.g., computer power).
Varying levels of sophistication have been incorporated into current cognitive models. For instance, by increasing the volume and breadth of procedural knowledge, larger ranges of behavioral response can be observed in a machine. Current models intended to shift the burden from a human user to a machine have been designed on common rule-based, case-based reasoning, or general situation-based approaches.
Such cognitive models, however, do not enable a machine to respond with behavioral variability consistent with actual populations. For example, military and law enforcement personnel are often confronted with situations that are highly ambiguous and involve personal interactions. In such situations, humans use not only rules, but also draw from a rich personal history to make decisions. The application of current models to such circumstances would not enable a machine to make the judgment calls required to reach a resolution.
It is therefore desirable to provide a solution that can reduce the dependency of human-machine interactions on the abilities of a human user and can enable a machine to more closely emulate human-like responses. The present invention can provide this through the use of a cognitive model based on human naturalistic decision-making processes, including pattern recognition and episodic memory. Machines based on the cognitive model of the present invention can use cognitive capacities fundamental to human-like communication and cooperation to interact with humans.